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| #read text file
data = pd.read_table('sensor_bis.txt', sep='\t')
#DataFrame
df = pd.DataFrame(data)
#Extraction of columns
df0=df['Acquisition date']
df1=df['Time of acquisition']
df2=df['ch0_1']
df3=df['ch1_1']
#definition of variables
ch0_1=[]
ch1_1=[]
date=[]
time=[]
#Initialization datetime.date object
ini=0
fin=int(len(df0)-1)#integer of initial data
date_i=df0[fin]
date_i=date_i.replace("/",' ')
date_i=date_i.split()
year_i=int(date_i[2])
month_i=int(date_i[1])
day_i=int(date_i[0])
time_i=df1[fin]
time_i=time_i.replace(":",' ')
time_i=time_i.split()
hour_i=int(time_i[0])
#min_i=int(time_i[1])
#sec_i=int(time_i[2])
#datetime object : "year_ / month_ / day_ 00:00:00"
dt_i = datetime.datetime.combine(datetime.date(year=year_i, month=month_i, day=day_i), datetime.time(hour=hour_i, minute=0, second=0))
#list date and time
for i in range(fin):
dt_i += datetime.timedelta(minutes=x)
d=dt_i.date().strftime("%d/%m/%Y")
t=dt_i.time().strftime("%H:%M:%S")
date.append(d)
time.append(t)
time.insert(0,"00:00:00")
#list parameters
for j in range(fin):
var0=0
var1=0
filter_ =((df1 >= time[j-1]) & (df1 <= time[j]) & (df0 == date[j-1]) & (df2!=9999))
var0='%.2f'%float(df2[filter_].mean())
var0=var0.replace("nan",'9999')
filter_ =((df1 <= time[j]) & (df1 >= time[j-1]) & (df0 == date[j]) & (df3!=9999))
var1='%.2f'%float(df3[filter_].mean())
var1=var1.replace("nan",'9999')
ch0_1.append(var0)
ch1_1.append(var1) |
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